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import os
import argparse
def eval_parse_args() -> argparse.Namespace:
""" This function parses the arguments passed to the script.
Returns:
argparse.Namespace: Namespace containing the arguments.
"""
parser = argparse.ArgumentParser(description="Multimodal Garment Designer argparse.")
# Diffusion parameters
parser.add_argument(
"--pretrained_model_name_or_path",
type=str,
default="runwayml/stable-diffusion-inpainting",
help="Path to pretrained model or model identifier from huggingface.co/models.",
)
parser.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
# destination folder
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="The output directory where the model predictions will be written.",
)
# Accelerator parameters
parser.add_argument(
"--mixed_precision",
type=str,
default=None,
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >="
" 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the"
" flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config."
),
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention", action="store_true", help="Whether or not to use xformers."
)
# dataset parameters
parser.add_argument("--dataset", type=str, required=True, choices=["dresscode", "vitonhd"], help="dataset to use")
parser.add_argument(
"--dataset_path",
type=str,
required=True,
help="Path to the dataset",
)
parser.add_argument("--category", type=str, default="", help="category to use")
parser.add_argument("--test_order", type=str, required=True, choices=["unpaired", "paired"],
help="Test order, should be either paired or unpaired")
# dataloader parameters
parser.add_argument("--batch_size", type=int, default=1, help="Batch size (per device) for the test dataloader.")
parser.add_argument("--num_workers_test", type=int, default=8,
help="Number of workers for the test dataloader.")
# input parameters
parser.add_argument("--mask_type", type=str, default="bounding_box", choices=["keypoints", "bounding_box"])
parser.add_argument("--no_pose", action="store_true", help="exclude posemap from input")
# disentagle classifier free guidance parameters
parser.add_argument("--disentagle", action="store_true")
parser.add_argument("--guidance_scale", type=float, default=7.5, help="text guidance scale, use with disentagle")
parser.add_argument("--guidance_scale_pose", type=float, default=7.5,
help="pose guidance scale, use with disentagle")
parser.add_argument("--guidance_scale_sketch", type=float, default=7.5,
help="sketch guidance scale, use with disentagle")
# sketch conditioninig paramters
parser.add_argument("--sketch_cond_rate", type=float, default=0.2, help="Sketch conditioning rate")
parser.add_argument("--start_cond_rate", type=float, default=0.0, help="offset sketch cond rate")
# miscelaneous parameters
parser.add_argument("--seed", type=int, default=1234, help="A seed for reproducible training.")
parser.add_argument("--save_name", type=str, required=True, help="Folder name of the saved images")
args = parser.parse_args()
# if not, set default local rank
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != args.local_rank:
args.local_rank = env_local_rank
return args
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